Compound object separation

ABSTRACT

Representations of an object ( 110 ) in an image generated by an imaging apparatus ( 100 ) can comprise two or more separate sub-objects, producing a compound object ( 500 ). Compound objects can negatively affect the quality of object visualization and threat identification performance. As provided herein, a compound object ( 500 ) can be separated into sub-objects. Three-dimensional image data of a potential compound object ( 500 ) is projected into a two-dimensional manifold projection ( 504 ), and segmentation is performed on the two-dimensional manifold projection of the compound object to identify sub-objects. Once sub-objects are identified, the two-dimensional, segmented manifold projection ( 900 ) is projected into three-dimensional space ( 1104 ). A three-dimensional segmentation may then be performed to identify additional sub-objects of the compound object that were not identified by the two-dimensional segmentation.

BACKGROUND

The present application relates to the field of x-ray and computedtomography (CT). It finds particular application with CT securityscanners. It also relates to medical, security, and other applicationswhere identifying sub-objects of a compound object would be useful.

Security at airports and in other travel related areas is an importantissue given today's sociopolitical climate, as well as otherconsiderations. One technique used to promote travel safety is baggageinspection. Often, an imaging apparatus is utilized to facilitatebaggage screening. For example, a CT device may be used to providesecurity personnel with two and/or three dimensional views of objects.After viewing images provided by the imaging apparatus, securitypersonnel may make a decision as to whether the baggage is safe to passthrough the security check-point or if further (hands-on) inspection iswarranted.

Current screening techniques and systems can utilize automated objectrecognition in images from an imaging apparatus, for example, whenscreening for potential threat objects inside luggage. These systems canextract an object from an image, and compute properties of theseextracted objects. Properties of scanned objects can be used fordiscriminating an object by comparing the objects properties (e.g.,density, shape, etc.) with known properties of threat items, non-threatitems, or both classes of items. It can be appreciated that an abilityto discriminate potential threats may be reduced if an extracted objectcomprises multiple distinct physical objects. Such an extracted objectis referred to as a compound object.

A compound object can be made up of two or more distinct items. Forexample, if two items are lying side by side and/or touching each other,a security scanner system may extract the two items as one singlecompound object. Because the compound object actually comprises twoseparate objects, however, properties of the compound object may not beable to be effectively compared with those of known threat and/ornon-threat items. As such, for example, luggage containing a compoundobject may unnecessarily be flagged for additional (hands-on) inspectionbecause the properties of the compound object resemble properties of aknown threat object. This can, among other things, reduce the throughputat a security checkpoint. Alternatively, a compound object that shouldbe inspected further may not be so identified because properties of apotential threat object in the compound object are “contaminated” orcombined with properties of one or more other (non-threat) objects inthe compound object, and these “contaminated” properties (of thecompound object) might more closely resemble those of a non-threatobject than those of a threat object, or vice versa.

Compound object splitting can be applied to objects in an attempt toimprove threat item detection, and thereby increase the throughput andeffectiveness at a security check-point. Compound object splittingessentially identifies potential compound objects and splits them intosub-objects. Compound object splitting involving components withdifferent densities may be performed using a histogram-based compoundobject splitting algorithm. Other techniques include using surfacevolume erosion to split objects. However, using erosion as a stand-alonetechnique to split compound objects can lead to undesirable effects. Forexample, erosion can reduce a mass of an object, and indiscriminatelysplit objects that are not compound, and/or fail to split some compoundobjects. Additionally, in these techniques, erosion and splitting may beapplied universally, without regard to whether an object is a potentialcompound object at all.

SUMMARY

Aspects of the present application address the above matters, andothers. According to one aspect, a method for splitting a potentialthree-dimensional compound objects is provided. The method comprisesprojecting three-dimensional image data indicative of a potentialthree-dimensional compound object under examination onto atwo-dimensional manifold projection and recording a correspondencebetween the three-dimensional image data (e.g., voxel data) and the 2Dmanifold projection (e.g., pixel data). The method also comprisessegmenting the two-dimensional manifold projection to generate atwo-dimensional segmented manifold projection indicative of one or moresub-objects. The method further comprises projecting the two-dimensionalsegmented manifold projection into three-dimensional image dataindicative of the sub-objects utilizing the correspondence between thethree-dimensional image data and the two-dimensional manifoldprojection.

According to another aspect, an apparatus is provided. The apparatuscomprises a projector configured to project three-dimensional image dataindicative of a potential compound object into a two-dimensionalmanifold projection indicative of the potential compound object. Theapparatus also comprises a two-dimensional segmentation componentconfigured to segment the two-dimensional manifold projection togenerate a two-dimensional segmented projection indicative of one ormore sub-objects of the potential compound object. The apparatus alsocomprises a back-projector configured to project the two-dimensionalsegmented projection into three-dimensional image data indicative of thesub-objects.

According to another aspect, a method is provided. The method comprisesprojecting three-dimensional image data indicative of a potentialcompound object under examination into a two-dimensional manifoldprojection of the potential compound object and recording acorrespondence between the three-dimensional image data and thetwo-dimensional manifold projection. The method also comprises erodingthe two-dimensional manifold projection using an adaptive erosiontechnique and segmenting the two-dimensional manifold projection togenerate a two-dimensional segmented projection indicative of one ormore sub-objects. The method further comprises pruning pixels indicativeof sub-objects of the two-dimensional segmented projection that do notmeet predetermined criteria and projecting the two-dimensional segmentedprojection into three-dimensional image data indicative of thecorresponding one or more sub-objects utilizing the correspondencebetween the three-dimensional image data and the two-dimensionalmanifold projection.

Those of ordinary skill in the art will appreciate still other aspectsof the present invention upon reading and understanding the appendeddescription.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram illustrating an example scanner.

FIG. 2 is a component block diagram illustrating one or more componentsof an environment wherein compound object splitting of objects in animage may be implemented as provided herein.

FIG. 3 is a component block diagram illustrating details of one or morecomponents of an environment wherein compound object splitting ofobjects in an image may be implemented as provided herein.

FIG. 4 is a flow chart diagram of an example method for compound objectsplitting.

FIG. 5 is a graphical representation of three-dimensional image data ofa compound object being converted onto a two-dimensional manifoldprojection.

FIG. 6 illustrates a portion of a two-dimensional manifold projection.

FIG. 7 illustrates a portion of a two-dimensional manifold projectionafter the projection has been eroded.

FIG. 8 is a graphical representation of a two-dimensional manifoldprojection that has been eroded.

FIG. 9 is a graphical representation of a two-dimensional manifoldprojection that has been segmented.

FIG. 10 is a graphical representation of a two-dimensional manifoldprojection that has been pruned.

FIG. 11 is a graphical representation of a two-dimensional, segmentedmanifold projection being projected into three-dimensional space.

FIG. 12 is a graphical representation of a compound object, a compoundobject after two-dimensional segmentation, and a compound object afterthree-dimensional segmentation.

FIG. 13 is an illustration of an example computer-readable mediumcomprising processor-executable instructions configured to embody one ormore of the provisions set forth herein.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the claimed subject matter. It may beevident, however, that the claimed subject matter may be practicedwithout these specific details. In other instances, structures anddevices are illustrated in block diagram form in order to facilitatedescribing the claimed subject matter.

Systems and techniques for separating a compound object representationinto sub-objects in an image generated by subjecting one or more objectsto imaging using an imaging apparatus (e.g., a computed tomography (CT)image of a piece of luggage under inspection at a security station at anairport) are provided herein. That is, in one embodiment, techniques andsystems for splitting compound objects into distinct sub-objects isprovided.

FIG. 1 is an illustration of an example environment 100 in which asystem may be employed for identifying potential threat containingobjects, from a class of objects, inside a container that has beensubjected to imaging using an imaging apparatus (e.g., a CT scanner). Inthe example environment 100 the imaging apparatus comprises an objectscanning apparatus 102, such as a security scanning apparatus (e.g.,used to scan luggage at an airport). The scanning apparatus 102 may beused to scan one or more objects 110 (e.g., a series of suitcases at theairport). The scanning apparatus typically comprises a rotating gantryportion 114 and a stationary gantry portion 116.

The rotating gantry portion 114 comprises a radiation source 104 (e.g.,an X-ray tube), an array of radiation detectors 106 (e.g., X-raydetectors), and a rotator 112 (e.g., a gantry motor) for rotating therotating gantry portion 114 (i.e., including the radiation source 104and detectors 106) around the object(s) being scanned 110. Anexamination surface 108 (e.g., a conveyor belt) passes through a hole inthe rotating gantry portion 114 and may be configured to convey theobject(s) 110 from an upstream portion of the object scanning apparatus102 to a downstream portion.

As an example, a computer tomography (CT) security scanner 102 thatincludes an X-ray source 104, such as an X-ray tube, can generate a fan,cone, wedge, or other shaped beam of X-ray radiation that traverses oneor more objects 110, such as suitcases, in an examination region. Inthis example, the X-rays are emitted by the source 104, traverse theexamination region that contains the object(s) 110 to be scanned, andare detected by an X-ray detector 106 across from the X-ray source 104.Further, a rotator 112, such as a gantry motor drive attached to thescanner, can be used to rotate the X-ray source 104 and detector 106around the object(s) 110, for example. In this way, X-ray projectionsfrom a variety of perspectives of the suitcase can be collected, forexample, creating a set of X-ray projections for the object(s). Whileillustrated with the x-ray source 104 and detector 106 rotating aroundan object, in another example, the radiation source 104 and detector 106may remain stationary while the object 110 is rotated.

In the example environment 100, a data acquisition component 118 isoperably coupled to the scanning apparatus 102, and is typicallyconfigured to collect information and data from the detector 106, andmay be used to compile the collected data into projection space data 150for an object 110. As an example, X-ray projections may be acquired ateach of a plurality of angular positions with respect to the object 110.Further, as the object(s) 110 is conveyed from an upstream portion ofthe object scanning apparatus 102 to a downstream portion (e.g.,conveying objects parallel to the rotational axis of the scanning array(into and out of the page)), the plurality of angular position X-rayprojections may be acquired at a plurality of points along the axis ofrotation with respect to the object(s) 110. In one embodiment, theplurality of angular positions may comprise an X and Y axis with respectto the object(s) being scanned, while the rotational axis may comprise aZ axis with respect to the object(s) being scanned.

In the example environment 100, an image extractor 120 is coupled to thedata acquisition component 118, and is configured to receive the data150 from the data acquisition component 118 and generatethree-dimensional image data 152 indicative of the scanned object 110using a suitable analytical, iterative, and/or other reconstructiontechnique (e.g., backprojecting from projection space to image space).

In one embodiment, the three-dimensional image data 152 for a suitcase,for example, may ultimately be displayed on a monitor of a terminal 132(e.g., desktop or laptop computer) for human observation. In thisembodiment, an operator may isolate and manipulate the image, forexample, rotating and viewing the suitcase from a variety of angles,zoom levels, and positions.

It will be appreciated that, while the example environment 100 utilizesthe image extractor 120 to extract three-dimensional image data from thedata 150 generated by the data acquisition component 118, for example,for a suitcase being scanned, the techniques and systems, describedherein, are not limited to this embodiment. In another embodiment, forexample, three-dimensional image data may be generated by an imagingapparatus that is not coupled to the system. In this example, thethree-dimensional image data may be stored onto an electronic storagedevice (e.g., a CD-ROM, hard-drive, flash memory) and delivered to thesystem electronically.

In the example environment 100, in one embodiment, an object and featureextractor 122 may receive the data 150 from the data acquisitioncomponent 118, for example, in order to extract objects and features 154from the scanned items(s) 110 (e.g., a carry-on luggage containingitems). It will be appreciated that the systems, described herein, arenot limited to having an object and feature extractor 122 at a locationin the example environment 100. For example, the object and featureextractor 122 may be a component of the image extractor 120, wherebythree-dimensional image data 152 and object features 154 are both sentfrom the image extractor 120. In another example, the object and featureextractor 122 may be disposed after the image extractor 120 and mayextract object features 154 from the three-dimensional image data 152.Those skilled in the art may devise alternative arrangements forsupplying three-dimensional image data 152 and object features 154 tothe example system.

In the example environment 100, an entry control 124 may receivethree-dimensional image data 152 and object features 154 for the one ormore scanned objects 110. The entry control 124 can be configured toidentify a potential compound object in the three-dimensional image data152 based on an object's features. In one embodiment, the entry control124 can be utilized to select objects that may be compound objects 156for processing by compound object splitting system 126. In one example,object features 154 (e.g., properties of an object in an image, such asan Eigen-box fill ratio) can be computed prior to the entry control 124and compared with pre-determined features for compound objects (e.g.,features extracted from known compound objects during training of asystem) to determine whether the one or more objects are compoundobjects. In another example, the entry control 124 calculates thedensity of a potential compound object and a standard deviation of thedensity. If the standard deviation is outside a predetermined range, theentry control 124 may identify the object as a potential compoundobject. Objects that are not determined to be potential compound objectsby the entry control 124 may not be sent through the compound objectsplitting system 126.

In the example environment 100, the compound object splitting system 126receives three-dimensional image data indicative of a potential compoundobject 156 from the entry control 124. The compound object splittingsystem 126 can be configured to generate sub-objects from the potentialcompound object by projecting the three-dimensional image data onto atwo-dimensional manifold projection (i.e., modeled on Euclidean space,for example) and recording a correspondence between thethree-dimensional image data (e.g., voxel data) and the two-dimensionalmanifold projection (e.g., pixel data). Once projected, one or morepixels indicative of the compound object in the two-dimensional manifoldprojection are eroded. Pixels that are not eroded may be segmented togenerate a two-dimensional segmented projection indicative of one ormore sub-objects of the potential compound object 156. It will beappreciated that where the potential compound object 156 is actually asingle object (and not a plurality of objects), the two-dimensionalsegmented projection may be indicative of a sub-object thatsubstantially resembles the potential compound object 156. Thetwo-dimensional segmented projection may then be projected fromtwo-dimensional manifold projection space to three-dimensional imagedata indicative of the sub-objects 158 utilizing the correspondencebetween the three-dimensional image data and the two-dimensionalmanifold data.

In the example environment 100, a three-dimensional segmentationcomponent 128 may be configured to receive the three-dimensional imagedata indicative of the sub-objects 158 and segment the three-dimensionalimage data indicative of the sub-object 158 to identify secondarysub-objects. The three-dimensional segmentation component 128 may alsobe configured to generate three-dimensional image data 160 indicative ofthe identified secondary sub-objects and/or the sub-objects (e.g.,identified by the compound object splitting system 126). It will beappreciated that if no secondary sub-objects are identified, thethree-dimensional image data 169 output by the three-dimensionalsegmentation component 128 may be indicative of the sub-objects.

In the example environment 100, a threat determiner 130 can receiveimage data for an object, which may comprise image data indicative ofsub-objects and/or image data indicative of secondary sub-objects. Thethreat determiner 130 can be configured to compare the image data to oneor more pre-determined thresholds, corresponding to one or morepotential threat objects. It will be appreciated that the systems andtechniques provided herein are not limited to utilizing a threatdeterminer, and may be utilized for separating compound objects withouta threat determiner. For example, image data for an object may be sentto a terminal 132 wherein an image of the object under examination 110may be displayed for human observation.

Information concerning whether a scanned object is potentially threatcontaining and/or information concerning sub-objects 162 can be sent toa terminal 132 in the example environment 100, for example, comprising adisplay that can be viewed by security personal at a luggage screeningcheckpoint. In this way, in this example, real-time information can beretrieved for objects subjected to scanning by a security scanner 102.

In the example environment 100, a controller 134 is operably coupled tothe terminal 132. The controller 134 receives commands from the terminal132 and generates instructions for the object scanning apparatus 102indicative of operations to be performed. For example, a human operatormay want to rescan the object 110 and the controller 134 may issue aninstruction instructing the examination surface 108 to reverse direction(e.g., bringing the object back into an examination region of the objectscanning apparatus 102).

FIG. 2 is a component block diagram illustrating one embodiment 200 ofan entry control 124, which can be configured to identify a potentialcompound object based on an object's features. The entry control 124 cancomprise a feature threshold comparison component 202, which can beconfigured to compare the respective one or more feature values 154 to acorresponding feature threshold 250.

In one embodiment, image data 152 for an object in question can be sentto the entry control 124, along with one or more corresponding featurevalues 154. In this embodiment, feature values 154 can include, but notbe limited to, an object's shape properties, such as an Eigen-box fillratio (EBFR) for the object in question. As an example, objects having alarge EBFR typically comprise a more uniform shape; while objects havinga small EBFR typically demonstrate irregularities in shape. In thisembodiment, the feature threshold comparison component 202 can compareone or more object feature values with a threshold value for that objectfeature, to determine which of the one or more features indicate acompound object for the object in question. In another embodiment, thefeature values 154 can include properties related to the average densityof the object and/or the standard deviation of densities of portions ofthe object. The feature threshold comparison component 202 may comparethe standard deviation of the densities to a threshold value todetermine whether a compound object may be present.

In the example embodiment 200, the entry control 124 can comprise anentry decision component 204, which can be configured to identify apotential compound object based on results from the feature thresholdcomparison component 202. In one embodiment, the decision component 204may identify a potential compound object based on a desired number ofpositive results for respective object features, the positive resultscomprising an indication of a potential compound object. As an example,in this embodiment, a desired number of positive results may be onehundred percent, which means that if one of the object featuresindicates a non-compound object, the object may not be sent to beseparated 160. However, in this example, if the object in question hasthe desired number of positive results (e.g., all of them) then theimage data for the potential compound object can be sent for separation156. In another example, the entry decision component 204 may identify apotential compound object when the standard deviation exceeds apredefined threshold at the threshold comparison component 202.

FIG. 3 is a component block diagram of one example embodiment 300 of acompound object splitting system 126, which can be configured togenerate three-dimensional image data 158 indicative of sub-objects fromthree-dimension image data 156 indicative of a potential compoundobject.

The example embodiment of the compound object splitter system 126comprises a projector 302 configured to receive the three-dimensionalimage data 156 indicative of the potential compound object. Theprojector is also configured to convert that three-dimensional imagedata 156 indicative of the potential compound object into atwo-dimensional manifold projection 350 indicative of the potentialcompound object and record a correspondence 351 between thethree-dimensional image data and the two-dimensional manifoldprojection. That is, one or more voxels of the three-dimensional imagedata are recorded as being represented by, or associated with, a pixelof the two-dimensional manifold projection 350 indicative of thepotential compound object. Such a recording may be beneficial duringback-projection from two-dimensional manifold projection space tothree-dimensional image space so that properties of the voxels (e.g.,densities of the voxels, atomic numbers identified by the voxels, etc.)are not lost during the projection and back-projection, for example. Itwill be appreciated that while the projector 302 records thecorrespondence 351 in this embodiment, in other embodiments, anothercomponent of the object splitter system 126 and/or the other componentsof the example environment 100 may record the correspondence 351.

It will be understood to those skilled in the art that a manifold is atwo-dimensional representation of a three-dimensional function. Forexample, a two-dimensional atlas of a portion of a globe may beconsidered a two-dimensional manifold projection. While a point on thetwo-dimensional atlas may not correspond perfectly with a correspondingpoint on a three-dimensional globe when viewed in the context ofneighboring points (e.g., because of the lack of a z-dimension, forexample), when viewed individually, or locally (e.g., not in contextwith neighboring points), the point is a substantially perfectrepresentation of the corresponding point on the three-dimensionalglobe.

In one example, the two-dimensional manifold projection 350 is mapped toEuclidean space. In this way, the manifold's dimension is a topologicalinvariant, and thus the two-dimensional manifold projection 350maintains topological properties of a given space in thethree-dimensional image data 156.

A pixel in the two-dimensional manifold projection 350 represents one ormore voxels of the three-dimensional image data 156. The number ofvoxels that are represented by a given voxel may depend upon the numberof object voxels that are “stacked” in a dimension of thethree-dimensional image data 156 that is not included in thetwo-dimensional manifold projection 350. For example, if at a given xand z coordinate, three voxels are stacked in y-dimension of thethree-dimensional image data 156, a pixel corresponding to the given xand z coordinate may represent three voxels in the two-dimensionalmanifold projection 350. Similarly, a pixel adjacent to the pixel mayrepresent five voxels if at a second x and z coordinate, five voxels arestacked in the y-dimension (e.g., the compound object has a largery-dimension at the x, z coordinates of the adjacent pixel than it doesat the pixel). The number of voxels represented by a pixel may bereferred to as a “pixel value”.

In the example embodiment 300, the compound object splitter system 126further comprises a manifold projection eroder 304 which is configuredto receive the two-dimensional manifold projection 350. The manifoldprojection eroder 304 is also configured to erode the two-dimensionalmanifold projection 350, and thus reveal one or more sub-objects of thepotential compound object. In one example, the manifold projectioneroder 304 uses an adaptive erosion technique to erode one or morepixels of the two-dimensional manifold projection 350, and thesub-objects are revealed based upon spaces, or gaps, within the compoundobject. It will be appreciated that an “adaptive erosion technique” asused herein refers to a technique that adjusts criteria, or thresholds,for determining which pixels to erode as a function of characteristicsof one or more (neighboring) pixels. That is, the threshold is notconstant, but rather changes according to the properties, orcharacteristics of the pixels.

In one example of an adaptive erosion technique, the manifold projectioneroder 304 determines whether to erode a first pixel by comparing pixelsvalues for pixels neighboring the first pixel to determine an erosionthreshold for the first pixel. Once the erosion threshold for the firstpixel is determined, the threshold is compared to respective pixelvalues of the neighboring pixels. If a predetermined number ofrespective pixel values are below the threshold, the first pixel iseroded (e.g., a value of the pixel is set to zero or some value notindicative of an object). The manifold projection eroder 304 may repeata similar adaptive erosion technique on a plurality of pixels toidentify spaces, or divides, in the compound object. In this way, one ormore portions of the compound object may be divided to reveal one ormore sub-objects (e.g., each “group” of pixels corresponding to asub-object). It will be appreciated that other adaptive techniquesand/or static techniques (e.g., where the threshold remains constantduring the erosion of a plurality of pixels) known to those skilled inthe art are also contemplated.

The compound object splitting system 126 further comprises atwo-dimensional segmentation component 306 configured to receive theeroded manifold projection 352 from the manifold projection eroder 304and to segment the two-dimensional manifold projection to generate atwo-dimensional, segmented, manifold projection 354. As an example,segmentation may include binning the pixels into bins corresponding to arespective sub-object and/or labeling pixels associated with identifiedsub-objects. For example, before erosion, the pixels may have beenlabeled with number “1”, indicative of (compound) object “1”. However,after erosion, one or more sub-objects of the (compound) object “1” maybe identified and a first group of pixels may be labeled according to avalue (e.g., “1”) assigned to a first identified sub-object, a secondgroup of pixels may be labeled according to a value (e.g., “2”) assignedto a second identified sub-object, etc. In this way, respectivesub-objects may be identified as distinct objects in the image, ratherthan a single compound object.

In the example embodiment 300, the compound object splitter system 126further comprises a pruner 308 that is configured to receive thetwo-dimensional, segmented, manifold projection 354. The pruner is alsoconfigured to prune pixels of the two-dimensional segmented manifoldprojection 354 that are indicative of sub-objects that do not meetpredetermined criteria (e.g., the sub-object is represented by too fewpixels to be considered a threat, the mass of the sub-object is notgreat enough to be a threat, etc.). In one embodiment, pruning comprisesrelabeling pixels indicative of the sub-objects that do not meetpredetermined criteria as background (e.g., labeling the pixels as “0”),or otherwise discarding the pixels. As an example, a sub-object that isrepresented by three pixels may be immaterial to achieving the purposeof the examination (i.e., threat detection), and the pruner may discardthe sub-object by altering the pixels.

The compound object splitting system 126 further comprises aback-projector 310 configured to receive the pruned and segmentedmanifold projection 356 and to project the two-dimensional manifoldprojection 356 into three-dimensional image data indicative of thesub-objects 158. That is, the back-projector 310 is configured toreverse map the data from two-dimensional manifold space intothree-dimensional image space utilizing the correspondence 351 betweenthe three-dimensional image data and the two dimensional manifoldprojection. In this way, voxels of the three-dimensional data indicativeof the potential compound object 156 may be relabeled according to thelabels assigned to corresponding pixels in the two-dimensional manifoldprojection 356 to generate the three-dimensional image data indicativeof the sub-objects 158. For example, voxels originally labeled asindicative of compound object “1” may be relabeled; a portion of thevoxels relabeled as indicative of sub-object “1” and a portion of thevoxels relabeled as indicative of sub-object “2.” It will be appreciatedthat by relabeling the voxels of the three-dimensional data indicativeof the potential compound object 156, properties of the voxels (andtherefore of the object) may be retained. Stated differently, by usingsuch a technique, the properties of the object may not be lost duringthe projection into manifold projection space and the projection frommanifold projection space into three-dimensional image space.

It will appreciated that in one embodiment, the three-dimensional imagedata indicative of the sub-objects 158 is segmented by athree-dimensional segmentation component (e.g., 128 in FIG. 1) thatfurther refines that object, or rather detects secondary sub-objectsthat were not identified by the compound object splitting system 126 togenerate three-dimensional data indicative of one or more secondarysub-objects. For example, where two objects substantially overlap in they-dimension and are connected to a third object, the compound objectsplitting system 126 may not recognize the substantially overlappingobjects if the manifold projection depicts that x and z dimensions.Therefore, the compound object splitting system 126 may separate thethree objects of the compound object into two sub-objects. A firstsub-object may comprise the two substantially overlapping objects andthe second sub-object may comprise the third sub-object. Thethree-dimensional segmentation component 128 may recognize a gap in they-dimension between the two sub-objects and separate the firstsub-object into two secondary sub-objects, for example. Thus, thecompound object splitting system 126 splits the compound object into twoobjects and the three-dimensional segmentation component 128 splits thetwo objects into three objects. It will be appreciated athree-dimensional segmentation component 128 placed before the compoundobject splitting system 126 may not recognize the two substantiallyoverlapping objects as two objects because both were connected to thethird object, and therefore the three-dimensional segmentation component128 would not have identified the gap in the y-dimension between the twooverlapping objects.

The three-dimensional image data indicative of the sub-objects 158and/or three-dimensional data indicative of the secondary sub-objects(e.g., 160 in FIG. 1) may be displayed on a monitor of a terminal (e.g.,132 in FIG. 1) and/or transmitted to a threat determiner (e.g., 130)that is configured to identify threats according to the properties of anobject. Because the compound object has been divided into sub-objects,the threat determiner may better discern the characteristics of anobject and thus may more accurately detect threats, for example.

A method may be devised for separating a compound object intosub-objects in an image generated by an imaging apparatus. In oneembodiment, the method may be used by a threat determination system in asecurity checkpoint that screens passenger luggage for potential threatitems. In this embodiment, an ability of a threat determination systemto detect potential threats may be reduced if compound objects areintroduced, as computed properties of the compound object may not bespecific to a single physical object. Therefore, one may wish toseparate the compound object into distinct sub-objects of which it iscomprised.

FIG. 4 is a flow chart diagram of an example method 400. Such an examplemethod 400 may be useful for splitting a potential three-dimensionalcompound object, for example. The method begins at 402 and involvesprojecting three-dimensional image data indicative of a potentialcompound object under examination onto a two-dimensional manifoldprojection of the potential compound object and a correspondence betweenthe three-dimensional image data and the two-dimensional manifoldprojection is recorded at 404. That is, the image data is mapped fromthree-dimensional image space to two-dimensional manifold projectionspace and voxel data one or more voxels of the image space are recordedas being associated with a pixel of the two-dimensional manifoldprojection. In one embodiment, the two-dimensional manifold projectionspace is Euclidean space.

It will be appreciated that before the three-dimensional image data isprojected into two-dimensional manifold projection space, it may beuseful to first identify whether an object is likely to be a potentialcompound object. In this way, the acts herein described may not beperformed unless it is probably that an identified object is a compoundobject. In one example, the probability that an object is a potentialcompound object is determined by calculating the average density and/oratomic number (i.e., if the scanner is a dual energy scanner) and astandard deviation. If the standard deviation is above a predefinedthreshold, the object may be considered a potential compound object andthus the acts herein described may be performed to split the potentialcompound object into one or more sub-objects.

FIG. 5 is a graphical representation of three-dimensional image data ofa compound object 500 being projected 502 onto a two-dimensionalmanifold projection 504. As illustrated, the three-dimensional object500 is collapsed into a two-dimensional plane that retains twodimensions (e.g., an x-dimension and a z-dimension) of thethree-dimensional object 500 (e.g., the y-dimension is lost during theprojection).

Because a dimension is lost when projecting from three-dimensional spaceto two-dimensional space, pixels of the two-dimensional manifoldprojection are assigned a value (herein referred to as a “pixel value”)based upon the number of voxels represented by the pixel. For example,if a y-dimension of the image data is lost during the projection, thepixel is assigned a value corresponding to the number of voxels in they-dimension that the pixel represents.

FIG. 6 illustrates an enlargement 600 of a portion of two-dimensionalmanifold projection 506 in FIG. 5. The squares 602 represent pixels inthe two-dimensional manifold. Pixels above a diagonal line 604 (e.g., anedge of a rectangular portion 508 of the object 500 in FIG. 5) arerepresentative of the rectangular portion 508. Pixels below an archedline 606 (e.g., an edge of an oval portion 510 of the object 500 in FIG.5) are representative of the oval portion 510. As illustrated,respective pixels are assigned a pixel value 608 (e.g., a number)corresponding to the number of voxels represented by the pixel. Forexample, pixels representative of the rectangular portion 508 have apixel value of nine because the rectangular portion 508 was representedby nine voxels in the y-dimension 512 (at all x and z dimensions of therectangle 508). Similarly pixels representative of the oval portion 510have a pixel value of three because the oval portion 510 was representedby three voxels in the y-dimension 514 (at all x and z dimensions of theoval 510). It will be appreciated that pixels that are representative ofboth of the oval portion 510 and the rectangular portion 508 (e.g.,pixels that are situated between the diagonal line 604 and the archedline 606) may be assigned a pixel value corresponding to the portion ofthe object represented by a larger number of voxels (e.g., the rectangle508).

Returning to FIG. 4, at 406 the two-dimensional manifold projection iseroded. That is, connections between two or more objects are removed sothat the objects are defined as a plurality of objects rather than asingle, compound object. Typically, eroding involves setting pixelsidentified with the connection to a value (e.g., zero) indicative of noobject, or rather indicative of background.

In one example, an adaptive erosion technique is used to erode thetwo-dimensional manifold projection. A determination of whether to erodepixels is dynamic (e.g., the erosion characteristics are not constant)and is based upon characteristics of pixels neighboring the pixel beingconsidered for erosion. That is, a threshold for determining whether toerode a pixel or not to erode a pixel is based upon characteristics ofneighboring pixels and the same threshold may not be used for each pixelthat is being considered for erosion. An adaptive erosion technique maybe beneficial over other erosion techniques known to those skilled inthe art to preserve portions of the object (e.g., 500 in FIG. 5) thatare located towards the interior of the object, or rather sub-objects,and portions of the object that are strongly connected based onprobability analysis (using a Markov random field model), for example.

As an example, the adaptive erosion technique used to determine whetherto erode a first pixel may comprise comparing pixel values (e.g., 608 inFIG. 6) for pixels neighboring the first pixel to determine an erosionthreshold for the first pixel. Once the erosion threshold for the firstpixel has been determined, it may be compared to respective pixel valuesof the neighboring pixels. If a predetermined number of respective pixelvalues of neighboring pixels are below the erosion threshold, the firstpixel may be eroded. These acts may be repeated to determine an erosionthreshold for a second pixel and to determine whether to erode thesecond pixel.

FIG. 7 illustrates the enlargement 600 in FIG. 6 after thetwo-dimensional manifold has been eroded. As illustrated, pixels wereeroded if at least four neighboring (e.g., in this case adjacent) pixelsdid not exceed the erosion threshold for the pixel under considerationfor erosion. The eroded pixels (e.g., 702) are represented by a pixelvalue of zero. The pixels that were not eroded maintained the pixelvalue that was assigned to them before the two-dimensional manifoldprojection was eroded.

FIG. 8 illustrates the two-dimension manifold projection 504 aftererosion (e.g., an eroded manifold projection). It will be appreciatedthat sub-objects of the compound object 500 have been defined and are nolonger in contact with one another (e.g., there is space 802 betweensub-objects). This may allow a two-dimensional segmentation component(e.g., 306 in FIG. 3) to more easily segment the compound object intosub-objects, for example.

Returning to FIG. 4, at 408 the two-dimensional manifold projection issegmented to generate a two-dimensional, segmented manifold projectionindicative of one or more sub-objects. Segmentation generally involvesbinning (e.g., grouping) pixels representative of a sub-object togetherand/or labeling pixels to associate the pixels with a particular object.For example, a suitcase may have a plurality of objects (each objectidentified by a label in the three dimensional image data). One object,identified by label “5” may be considered a potential compound objectand thus image data of the potential object may be converted to manifoldprojection space and each pixel may be identified by the label “5”(e.g., corresponding to the object being examined). After the manifoldprojection is eroded, two sub-objects may be identified and the pixelsmay be relabeled (e.g., segmented). A first sub-object may be labeled“5,” for example, and a second sub-object may be labeled “6.” In thisway, two sub-objects may be identified from a single potential compoundobject.

FIG. 9 illustrates a two-dimensional segmented, manifold projection 900indicative of three objects. Pixels indicative of a rectangularsub-object 902 are labeled with a first label, pixels indicative of anoval sub-object 904 are labeled with a second label, and pixelsindicative of a circular sub-object 906 are labeled with a third label.Stated different, pixels of the two-dimensional manifold 505 that wereoriginally indicative of a single potential compound object 500 are nowindicative of three sub-objects. It will be appreciated that the shadingin FIG. 9 is only intended to represent the recognition of sub-objects,rather than a single compound object, and is not intended to representcoloring or shading of the manifold projection 900.

At 410 in FIG. 4, pixels indicative of sub-objects of thetwo-dimensional segmented projection that not meet predeterminedcriteria are pruned (e.g., the pixels are set to a background value).The predetermined criteria may include a pixel count for the sub-object(e.g., a number of pixels representative of the sub-object), that massof the sub-object, and/or another criteria that would help determinewhether the sub-object is valuable to the examination and thereforeshould not be pruned. For example, pixels that are indicative of asub-object that is unlikely to be a threat because of the size of thesub-object may be removed so that time is not consumed backprojectingthe pixels into three-dimensional space. In FIG. 10, the circularsub-object 906 of the two-dimensional segmented projection 900 is pruned1002 because the number of pixels representing the circular sub-object906 were too few to indicate that the sub-object was a security threat,for example.

At 412 in FIG. 4, the two-dimensional segmented projection is projectedinto three-dimensional image data indicative of the sub-objectsutilizing the correspondence between the three-dimensional image dataand the two-dimensional manifold projection. In one example, thisincludes relabeling voxels of the three-dimensional image data (e.g.,156 in FIG. 1) indicative of the potential compound object according tothe labels of corresponding pixels in the two-dimensional, segmentedmanifold projection. For example, if voxels of the potential compoundobject were labeled as belonging to object “5” in a suitcase, the voxelsmay be relabeled so that some of the voxels are indicative of arectangular object (labeled “5”) and some of the voxels are indicativeof a circular object (labeled “6”). In this way, data that is determinedto be indicative of a compound object it split into a pluralitysub-objects.

FIG. 11 provides a graphical representation of the two-dimensionalsegmented, manifold projection 900 being projected 1102 intothree-dimensional image data indicative of one or more sub-objects 1104.As illustrated by the shading, the rectangular object 1106 is recognizedas a first object and the oval object 1108 is recognized as a secondobject (e.g., the objects are no longer recognized as parts of acompound object 500).

In one embodiment, the three-dimensional image data indicative of thesub-objects may be segmented to further segment the sub-objects andidentify one or more secondary sub-objects. A segmentation inthree-dimensional image data after the two-dimensional segmentation maybe useful to identify an object that substantially overlays a secondobject in the dimension that was lost when the three-dimensional imagewas converted to two-dimensional space (e.g., the y-dimension), andthus, could not be segmented in the two-dimensional space.

FIG. 12 depicts a series of representations of a three-dimensionpotential compound object being split through the acts herein describe.The first representation 1202 represents the potential compound objectbefore the image data is converted to manifold projection space and/ortwo-dimensional segmentation has occurred. As illustrated, a first 1204,a second, 1206, and a third 1208 rectangular object are part of thecompound object.

The second representation 1210 represents the compound object after themanifold projection data has been segmented and projected back intothree-dimensional image space. As illustrated, during segmentation inthe two-dimensional manifold space, the compound object was recognizedto be two-sub-objects. The first rectangular object 1204 (e.g.,illustrated in a first shading pattern) was recognized as a differentobject that the second 1206 and third 1208 rectangular objects (e.g.,illustrated in a second shading pattern). However, because the second1206 and third 1208 rectangular objects were “stacked” on top of oneanother (e.g., the objects lie in the same x and z dimensions), thetwo-dimensional segmentation could not identify a “gap,” or weakconnection, between the second 1206 and third 1208 rectangular objects.Therefore, voxels associated with the second rectangular object 1206 andvoxels associated with the third rectangular object 1208 were labeled asbeing as a single sub-object. It will be appreciated that athree-dimensional segmentation prior to the two-dimensional segmentationwould also not recognize the second 1206 and third 1208 rectangularobjects as separate objects because they were both joined to the firstrectangular object 1204.

The third representation 1212 represents the compound object after athree-dimensional segmentation has occurred. Because the firstrectangular object 1204 was recognized as a separate sub-object of thecompound object during two-dimensional manifold segmentation, athree-dimension segmentation may identify a “gap” between the second1206 and third 1208 rectangular objects and split the sub-object intotwo secondary sub-objects (e.g., each object is represented by adifferent shading pattern). Thus, the single compound object (e.g.,represented by the first representation 1202) is split into threeobjects by performing a two-dimensional segmentation and athree-dimensional segmentation.

Returning to FIG. 4, the method ends at 414.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to implement one or more ofthe techniques presented herein. An example computer-readable mediumthat may be devised in these ways is illustrated in FIG. 13, wherein theimplementation 1300 comprises a computer-readable medium 1302 (e.g., aCD-R, DVD-R, or a platter of a hard disk drive), on which is encodedcomputer-readable data 1304. This computer-readable data 1304 in turncomprises a set of computer instructions 1306 configured to operateaccording to one or more of the principles set forth herein. In one suchembodiment 1300, the processor-executable instructions 1306 may beconfigured to perform a method 1308, such as the example method 400 ofFIG. 4, for example. In another such embodiment, theprocessor-executable instructions 1306 may be configured to implement asystem, such as at least some of the exemplary scanner 100 of FIG. 1,for example. Many such computer-readable media may be devised by thoseof ordinary skill in the art that are configured to operate inaccordance with one or more of the techniques presented herein.

Moreover, the words “example” and/or “exemplary” are used herein to meanserving as an example, instance, or illustration. Any aspect, design,etc. described herein as “example” and/or “exemplary” is not necessarilyto be construed as advantageous over other aspects, designs, etc.Rather, use of these terms is intended to present concepts in a concretefashion. As used in this application, the term “or” is intended to meanan inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X employs A or B” isintended to mean any of the natural inclusive permutations. That is, ifX employs A; X employs B; or X employs both A and B, then “X employs Aor B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims may generally be construed to mean “one or more” unless specifiedotherwise or clear from context to be directed to a singular form.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated example implementations of thedisclosure. In addition, while a particular feature of the disclosuremay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes”, “having”, “has”, “with”, or variants thereof areused in either the detailed description or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

What is claimed is:
 1. A method for splitting a potentialthree-dimensional compound object, comprising: projectingthree-dimensional image data indicative of the potentialthree-dimensional compound object along a first dimension to generate atwo-dimensional manifold projection from the three-dimensional imagedata and recording a correspondence between the three-dimensional imagedata and the two-dimensional manifold projection, wherein each pixel inthe two-dimensional manifold projection represents a stack of voxels inthe three-dimensional image data that extend along the first dimension;segmenting the two-dimensional manifold projection to generate atwo-dimensional segmented manifold projection indicative of one or moresub-objects, the segmenting comprising eroding a portion of thetwo-dimensional manifold projection; and projecting the two-dimensionalsegmented manifold projection into three-dimensional image dataindicative of the sub-objects utilizing the correspondence between thethree-dimensional image data and the two-dimensional manifoldprojection.
 2. The method of claim 1, comprising identifying thepotential three-dimensional compound object based on characteristics ofthe potential three-dimensional compound object.
 3. The method of claim1, wherein segmenting the two-dimensional manifold projection comprises:grouping pixels of the two-dimensional manifold projection to generatethe two-dimensional segmented manifold projection, wherein a group ofpixels is indicative of a sub-object.
 4. The method of claim 3,comprising pruning pixels indicative of sub-objects of thetwo-dimensional segmented manifold projection that do not meetpredetermined criteria.
 5. The method of claim 4, wherein thepredetermined criteria comprises a pixel count threshold.
 6. The methodof claim 1, wherein eroding a portion of the two-dimensional manifoldprojection comprises: comparing pixel values for pixels neighboring afirst pixel to determine an erosion threshold for the first pixel;comparing the erosion threshold for the first pixel to respective pixelvalues of the pixels neighboring the first pixel; and eroding the firstpixel if a predetermined number of the respective pixel values of thepixels neighboring the first pixel are below the erosion threshold forthe first pixel.
 7. The method of claim 3, comprising labeling a firstgroup of pixels of the two-dimensional manifold projection with a firstlabel and a second group of pixels of the two-dimensional manifoldprojection with a second label if there is a second group, the firstlabel different from the second label.
 8. The method of claim 1,comprising reconstructing the three-dimensional image data fromprojection space data.
 9. The method of claim 1, wherein a thresholdvalue for determining whether to erode a pixel is dynamic and based uponcharacteristics of pixels neighboring the pixel.
 10. The method of claim1, wherein segmenting the two-dimensional manifold projection comprises:identifying one or more sub-objects using the two-dimensional manifoldprojection; and labeling pixels associated with a first identifiedsub-object of the two-dimensional manifold projection with a first labeland labeling pixels associated with a second identified sub-object ofthe two-dimensional manifold projection with a second label if a secondsub-object is identified.
 11. The method of claim 1, comprisingsegmenting the three-dimensional image data indicative of thesub-objects to identify one or more secondary sub-objects.
 12. Anapparatus, comprising: a processor; and memory comprising instructionsthat when executed at least in part by the processor perform operations,comprising: projecting three-dimensional image data of a potentialcompound object along a first dimension to generate a two-dimensionalmanifold projection indicative of the potential compound object from thethree-dimensional image data, wherein each pixel in the two-dimensionalmanifold projection represents a stack of voxels in thethree-dimensional image data that extend along the first dimension;eroding one or more pixels of the two-dimensional manifold projection;segmenting, after the eroding, the two-dimensional manifold projectionto generate a two-dimensional segmented manifold projection indicativeof one or more sub-objects of the potential compound object; andprojecting the two-dimensional segmented manifold projection intothree-dimensional image data indicative of the sub-objects.
 13. Theapparatus of claim 12, the segmenting comprising labeling a first groupof pixels of the two-dimensional manifold projection according to avalue assigned to a first identified sub-object and labeling a secondgroup of pixels of the two-dimensional manifold projection according toa value assigned to a second identified sub-object if a secondsub-object is identified.
 14. The apparatus of claim 12, wherein theeroding comprises using an adaptive erosion technique to erode the oneor more pixels of the two-dimensional manifold projection.
 15. Theapparatus of claim 12, comprising pruning two-dimensional segmentedmanifold projection, the pruning comprising eroding a set of one or morepixels that do not meet predetermined criteria.
 16. The apparatus ofclaim 12, comprising segmenting the three-dimensional image dataindicative of the sub-objects to identify one or more secondarysub-objects.
 17. The apparatus of claim 12, the projecting thetwo-dimensional segmented manifold projection comprising labeling one ormore voxels of the three-dimensional image data indicative of thepotential compound object according to labels of pixels in thetwo-dimensional segmented manifold projection that correspond to thevoxels.
 18. A method, comprising: projecting three-dimensional imagedata indicative of a potential compound object under examination along afirst dimension to generate a two-dimensional manifold projection of thepotential compound object from the three-dimensional image data andrecording a correspondence between the three-dimensional image data andthe two-dimensional manifold projection, wherein each pixel in thetwo-dimensional manifold projection represents a stack of voxels in thethree-dimensional image data that extend along the first dimension;eroding the two-dimensional manifold projection using an adaptiveerosion technique; segmenting the two-dimensional manifold projection togenerate a two-dimensional segmented manifold projection indicative ofone or more sub-objects; pruning pixels indicative of sub-objects of thetwo-dimensional segmented manifold projection that do not meetpredetermined criteria; and projecting the two-dimensional segmentedmanifold projection into three-dimensional image data indicative of theone or more sub-objects utilizing the correspondence between thethree-dimensional image data and the two-dimensional manifoldprojection.
 19. The method of claim 18, wherein the adaptive erosiontechnique comprises: comparing pixel values for pixels neighboring afirst pixel to determine an erosion threshold for the first pixel;comparing the erosion threshold for the first pixel to respective pixelvalues of the pixels neighboring the first pixel; and eroding the firstpixel if a predetermined number of the respective pixel values of thepixels neighboring the first pixel are below the erosion threshold forthe first pixel.
 20. The method of claim 19, wherein eroding thetwo-dimensional manifold projection comprises: comparing pixel valuesfor pixels neighboring a second pixel to determine an erosion thresholdfor the second pixel, wherein the erosion threshold for the second pixelis different than the erosion threshold for the first pixel; comparingthe erosion threshold for the second pixel to respective pixel values ofthe pixels neighboring the second pixel; and eroding the second pixel ifa predetermined number of the respective pixel values of the pixelsneighboring the second pixel are below the erosion threshold for thesecond pixel.